An Overturned Charge Injection Synaptic Transistor With a Floating-Gate for Neuromorphic Hardware Computing

Kim, Myung‐Su; Kim, Jin‐Ki; Yun, Gyeong‐Jun; Yu, Ji‐Man; Han, Joon‐Kyu; Lee, Jung-Woo; Seo, Seokho; Choi, Sung Hi; Choi, Yang‐Kyu

doi:10.1109/led.2022.3194556

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Myung et al [105] proposed a modified FG transistor called overturned charge injection synaptic transistor (OCIST) specifically for neuromorphic computing. The key distinction of OCIST lies in the incorporation of an additional layer known as the Charge Valve Layer (CVL), setting it apart from the conventional FG transistors.…”

Section: Integration Of Transistors In Neuromorphic Systemsmentioning

confidence: 99%

FETs for Analog Neural MACs

Das,

Rajalekshmi,

Pallathuvalappil

et al. 2024

IEEE Access

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This study provides a comprehensive view on neural network systems with implemented with crossbar circuits, and device-level understanding of modern FET technologies in neuromorphic computing. This work categorizes and analyzes various transistor types, including ion-gate, ferroelectric, and floatinggate transistors, shedding light on their unique advantages and applications in neuromorphic computing. In this overview, we explore the fundamental principles, recent advancements, and significant trends in transistor-based neuromorphic devices, providing valuable insights into this innovative field. This work also examines resistive memories and 2D materials, that could revolutionize transistor fabrication for neuromorphic devices. Further, various research challenges, limitations, and potential research directions are discussed.INDEX TERMS Field Effect Transistor (FET), CMOS, Memristors, Multiply and Accumulate (MAC), 2D materials, FeFET, IGT.

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Section: Integration Of Transistors In Neuromorphic Systemsmentioning

confidence: 99%

FETs for Analog Neural MACs

Das,

Rajalekshmi,

Pallathuvalappil

et al. 2024

IEEE Access

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show abstract

“…Therefore, memory-based neuromorphic computing systems may offer great potential to achieve efficient brain-mimicked information processes as well as sophisticated cognitive functions. For instance, two-terminal memory devices, such as resistive switching memory [ 4 , 5 ], phase-change memory [ 6 , 7 ], and ferroelectric memory [ 8 , 9 ], and three-terminal devices, such as ferroelectric gate-oxide memory [ 10 , 11 ], charge-trap memory [ 12 , 13 ], and floating-gate memory [ 14 , 15 ], are feasible examples that can demonstrate the analog synaptic memory functions.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Memristive Synapse Composed of Ferroelectric ZnVO-Based Schottky Junction

Lee,

Hong,

Sekar

et al. 2024

Nanomaterials

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In pursuit of realizing neuromorphic computing devices, we demonstrated the high-performance synaptic functions on the top-to-bottom Au/ZnVO/Pt two-terminal ferroelectric Schottky junction (FSJ) device architecture. The active layer of ZnVO exhibited the ferroelectric characteristics because of the broken lattice-translational symmetry, arising from the incorporation of smaller V5+ ions into smaller Zn2+ host lattice sites. The fabricated FSJ devices displayed an asymmetric hysteresis behavior attributed to the ferroelectric polarization-dependent Schottky field-emission rate difference in between positive and negative bias voltage regions. Additionally, it was observed that the magnitude of the on-state current could be systematically controlled by changing either the amplitude or the width of the applied voltage pulses. Owing to these voltage pulse-tunable multi-state memory characteristics, the device revealed diverse synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and versatile rules in spike time-dependent synaptic plasticity. For the pattern-recognition simulation, furthermore, more than 95% accuracy was recorded when using the optimized experimental device parameters. These findings suggest the ZnVO-based FSJ device holds significant promise for application in next-generation brain-inspired neuromorphic computing systems.

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Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference

Kim

Park

et al. 2023

Nano Lett.

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The coming of the big-data era brought a need for power-efficient computing that cannot be realized in the Von Neumann architecture. Neuromorphic computing which is motivated by the human brain can greatly reduce power consumption through matrix multiplication, and a device that mimics a human synapse plays an important role. However, many synaptic devices suffer from limited linearity and symmetry without using incremental step pulse programming (ISPP). In this work, we demonstrated a charge-trap flash (CTF)-based synaptic transistor using trap-level engineered Al 2 O 3 /Ta 2 O 5 /Al 2 O 3 gate stack for successful neuromorphic computing. This novel gate stack provided precise control of the conductance with more than 6 bits. We chose the appropriate bias for highly linear and symmetric modulation of conductance and realized it with very short (25 ns) identical pulses at low voltage, resulting in low power consumption and high reliability. Finally, we achieved high learning accuracy in the training of 60000 MNIST images.

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An Overturned Charge Injection Synaptic Transistor With a Floating-Gate for Neuromorphic Hardware Computing

Cited by 4 publications

References 22 publications

FETs for Analog Neural MACs

FETs for Analog Neural MACs

High-Performance Memristive Synapse Composed of Ferroelectric ZnVO-Based Schottky Junction

Dielectric-Engineered High-Speed, Low-Power, Highly Reliable Charge Trap Flash-Based Synaptic Device for Neuromorphic Computing beyond Inference

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